Entrepreneur, tech guy, trouble shooter, attorney. GC @CIC_Health, Managing Director @cicnow, founder @cicboston, founder @spacewithasoul. Opinions are my own.
Boston, MA
Joined April 2009
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Stas Gayshan retweeted
anthropic dropped a 154 page threat report and some of this shit is absolutely insane.
> alibaba allegedly sent 151M+ exchanges to claude to help train qwen
> moonshot and deepseek allegedly secretly routed millions of real user conversations through claude while users thought they were using kimi and deepseek
> a weapons group used multiple claude instances as an engineering team, test fired a guided rocket, failed, then asked claude to figure out what went wrong
> zhipu used 273 fraudulent accounts to extract claude’s chain of thought and used claude to clean, score and improve its own training pipeline
> a russian hacking group used claude across an espionage campaign and even had malware rewrite itself when security software detected it
> researchers tried using claude for potentially dangerous biological research
> governments used claude for surveillance of dissidents, journalists and activists
what the actual fuck
We're publishing our most detailed threat intelligence report to date.
It covers how people tried to misuse Claude—for cyberattacks, influence operations, surveillance, biology, and building weapons—and how we found and stopped them.
We disrupted every operation in the report, and used the lessons from them to strengthen our safeguards. Where appropriate, we also shared what we found with authorities and other AI companies.
These cases are not typical: we’re highlighting some of the most sophisticated misuse we’ve seen. But they’re especially important to discuss, because they show us where AI misuse is headed, where our safeguards work, and where they need to improve.
We’re publishing this report so others can spot the same activity on their own platforms, and so we can give the public a clearer view of how emerging threats develop.
Read the report: anthropic.com/threat-intelli…
Stas Gayshan retweeted
Anthropic released Claude Fable 5 yesterday. Two numbers from the launch tell you everything about where AI in legal actually stands.
93.4% and 13.3%.
On BigLaw Bench, Harvey's benchmark for legal task performance, Fable 5 scored 93.4% - a new high for any Anthropic model. Harvey's lawyer evaluators praised its drafting and markup work: it reliably caught term sheet deviations, off-market provisions, and internal inconsistencies in counterparty redlines.
On Harvey's Legal Agent Benchmark - which measures end-to-end completion of complex legal tasks under a strict all-pass standard - it scored 13.3%.
That 13.3% is an all-time high. The previous best, Opus 4.8, scored 10.4%.
Note the phrase "all-pass standard." One miss and the task fails. Which is exactly how legal work is judged. A brief with one fabricated citation is not 95% good. It is a sanctions hearing.
Read the two numbers together.
The most capable AI model ever made publicly available - the first of Anthropic's "Mythos-class" tier, state-of-the-art on nearly every benchmark they tested - drafts and marks up at the highest level ever measured, and still fails roughly 87% of complete legal matters under the standard lawyers are actually held to.
This is not a flaw the next release will patch. It has been the shape of every release for two years. Task performance climbs fast. End-to-end performance crawls - 10.4 to 13.3 is the frontier moving at full speed.
The gap between 93.4 and 13.3 has a name.
It is the lawyer.
The judgment about which draft is the right draft. The sequencing of a matter. The knowledge of what the client actually meant, what opposing counsel will do with a weak sentence, what this judge will tolerate. None of that is in the 93.4. All of it is in the missing 87.
So two groups of lawyers are wrong today.
The ones waiting for AI to replace lawyers - the benchmark just told you it cannot run a matter, and the best lab in the world moved that number three points in one generation.
And the ones ignoring AI until it is "ready" - you are leaving the strongest drafting and markup tool ever built sitting unused while you do that work by hand at 11 PM.
The lawyers who win the next five years are not waiting for 13.3 to become 90.
They are using the 93.4 today and being the reason the work ships correct.
Stas Gayshan retweeted
Trump, Musk and Rubio slashed aid and scoffed that it was woke nonsense. Now they're seeing that it not only saved one life every 10 seconds but also protected us from diseases like Ebola. Their actions constituted a security failure as well as a moral one.
More broadly, their fecklessness contrasts with the courage and humanity of doctors and aid workers in Congo and Uganda, lacking adequate PPE but still risking the virus to care for fellow humans.
Trump, Musk and Rubio might learn something from them. nytimes.com/2026/06/03/opini…
Stas Gayshan retweeted
I hope all the tech bros are listening even the ones in the back.
The thing with lawyers was that drafting a document was never the job. Doing research was never the job.
Each was a task.
A task isn’t a job. The purpose of the lawyer’s job is to solve legal problems for the client and provide the comfort and accountability around and as part of these solutions. That’s what people need from lawyers.
The fact that lawyers can now do the drafting, analysis, or researching faster or better with AI just made lawyers more needed and more valuable. If legal AI is used in the right way, imagine the scale that will be given to lawyers to solve more and more complex legal problems for clients. Their purpose and the need for their services will compound.
As the world gets more complex, society needs more lawyers to help people and businesses with their legal problems. The solution isn’t for clients to solve them on their own with AI slop because they will suffer harm, loss, and make the wrong decisions based on inaccurate, inexperienced, and wrong information, documents, analysis, and advice.
I’ve said this before. Tech bros love to predict the end of jobs that they don’t understand because it fits their agenda not the reality based on real, deep understanding of the job or clients’ needs. That’s just stupid and irresponsible. But that’s life.
With legal AI being used correctly, effectively, and responsibly by lawyers, we will see more lawyers being able to solve more and more complex legal problems for people and businesses at scale.
Lawyer are just being given new superhuman powers. Lawyers and legal services are just getting started.
Stas Gayshan retweeted
Iowa has one of the fairest maps in the country.
They got it through a truly exceptional set of unique rules:
(1) Countries cannot be split.
(2) A completely non-partisan agency draws maps.
(3) This agency cannot look at most demographic data (including the partisan makeup of an area) or the addresses of incumbents.
(4) State senate districts must follow the boundaries of Congressional districts.
(5) Each state senate district contains two house districts that are fully contained within its boundaries.
Stas Gayshan retweeted
Anthropic just shipped sleep into agents.
When you sleep, your hippocampus replays the day's neural sequences to the cortex during 150-220 Hz bursts called sharp-wave ripples. The replay runs about 20x faster than the original experience. A 10-second sequence gets compressed to roughly 500 milliseconds. Wilson and McNaughton showed this in rats in 1994. You ran this algorithm last night on whatever you did yesterday, whether you wanted to or not.
The replay does two things at once. It extracts statistical patterns: what mattered, what generalizes, which sequences predicted reward. And it reorganizes the memory trace from hippocampus-dependent storage into neocortex, which is why old memories survive hippocampal damage but recent ones don't. Disrupt sharp-wave ripples in a rat with optogenetics and the rat fails the next day's task. The replay is causal, not correlational.
Most "agent memory" today is a search engine. Past sessions get embedded, you retrieve relevant chunks at the next call. That works for facts. It does not extract patterns and it does not reorganize the trace. Which is why agents plateau. The memory volume keeps growing while real capability flatlines.
Dreaming reviews past sessions, extracts patterns, curates memories. That is the brain's actual three-step algorithm. They called it dreaming because dreaming is what the algorithm does, in roughly the same order, for roughly the same reason.
Agents that dream between sessions will compound. The ones still running on raw context window will hit the same ceiling humans hit when they pull all-nighters.
Stas Gayshan retweeted
Second-time Founders is my favourite gender :
1) no deck until someone asks three times
2) first hire is a lawyer
3) distribution for the product before the product exists
4) "we don't need a big round" and means it this time
5) replies to every customer email personally because they know what ignoring customers cost them last time
6) sleeps 8 hours and ships faster than everyone else
7) the only person in the room who isn't impressed by the term sheet
Second-time founders are the best breed of founders
Stas Gayshan retweeted
the craziest part now is that the modern computer probably has to be entirely reinvented, from scratch. pretty much like how jobs & co brought apple ii to market.
like not improved. not given a chatbot sidebar or something but really from the ground up like the iphone redefined what it meant to be a pocket computer.
the current paradigm for computers was built around a human staring at a screen, moving a cursor, opening apps, managing windows, naming files, remembering where things live, & manually translating intent into interface actions.
that made sense when the human was the runtime. but in an ai native world, it starts to look kinda ridiculous.
you can see this ridiculousness when you use computer use agents… they are useful sure, but they’re also obviously transitional. they’re teaching ai to operate machines designed for humans, which is clever, but also kind of absurd. it’s like making a robot hand so it can use a doorknob instead of asking why the door needs a knob at all. yes i know humans also need to use a door knob, but maybe in the future humans don’t need to use a computer, or at least what we think of a computer today at all.
this all leads to some interesting questions:
- what is a file when the system understands context?
- what is an app when intent can route itself?
- what is a desktop when work can be decomposed, executed, monitored, & summarized by agents?
- what is a browser when the agent can retrieve, compare, transact, & remember?
- what is an operating system when the primary user is no longer just a person, but a person plus a swarm of delegated intelligences? or no person at all.
the old computer assumed navigation.
the new computer has to assume a new kind of intention. the old computer organized information. the new computer has to try to organize agency.
we’re still in the hacky middle stage at the moment with sidebars, copilots, agents clicking through legacy ui, & automation layers sitting on top of 40 year old metaphors.
the new computer is likely one where memory, context, identity, permissions, tools, agents, & interfaces are native primitives. this means desktop, mobile, browser, apps, files, folders deserves another first principles look.
Stas Gayshan retweeted
A parasite that has been eating people for 3,500 years is about to be wiped off the planet. It infected 3.5 million people in 1986. Last year, it infected 10. And I have not seen it make a single front page.
It is called Guinea worm. You drink contaminated water from a pond in a poor village. A year later, a worm up to three feet long starts coming out of your leg through a burning blister. There is no pill that stops it and no surgery that works. You wrap the worm around a stick and pull it out slowly, over days or weeks, inch by inch. If you rush, the worm breaks inside you and causes a fresh infection.
Guinea worm is ancient. Preserved worms have been pulled out of Egyptian mummies from around 1000 BCE. The Ebers Papyrus, an Egyptian medical scroll from 1550 BCE, describes pulling the worm out with a stick. For three and a half thousand years, that was the best humans could do.
Then in 1986, public health workers decided to kill the parasite off. They had no vaccine and no drug. What they had was cheap cloth water filters and a small army of volunteers willing to walk from village to village for decades.
The plan was simple. Give everyone who drinks from a pond a cloth filter to strain out the tiny water fleas that spread the parasite. Then send volunteers walking house to house, year after year, teaching people how to use the filters and keeping anyone with an emerging worm out of the water.
It worked. From 3.5 million cases a year to 10. Four were in Chad, four in Ethiopia, two in South Sudan. The other four countries where the worm used to be common, Angola, Cameroon, the Central African Republic, and Mali, had zero human cases for the second year in a row. The World Health Organization has already certified 200 countries as Guinea worm free. Six are left.
The last hurdle is dogs. Cameroon had 445 infected animals last year and Chad had 147, so a lot of the remaining work is on animals, not humans. Strays get leashed, and crews treat ponds to kill any remaining worms. The campaign keeps watching until the number hits zero.
When Guinea worm hits zero, it becomes the second human disease ever erased from the planet. The first was smallpox. It will also be the first parasite humans have ever wiped out, and the first disease ever ended without a single dose of medicine. Volunteers walked village to village with cloth filters for 40 years. Now a plague from the age of the pharaohs is about to be gone.
Stas Gayshan retweeted
I agree with this fully. There is a totally new role emerging here. It's a net new role, and requires a somewhat unique set of skills.
This is a nascent idea / stream of conciousness, but the reason I know it exists is because this is essentially what I am doing right now for a handful of companies.
Skills that are useful for this role:
- Systems thinking
- Being good at interviewing people to understand what they do and asking good questions.
- Building diagrams / mental models of how work flows within an organization
- Being on the leading edge of agentic coding platforms (e.g. Claude Code)
- Experimentation mindset
- Asking questions until you fully understand the job to be done
- Realizing that sometimes the job to be done is to completely change the job to be done
- Communicating across different functions, but in a way that forces changes versus build alignment
- Courage to try new things
Lots of other stuff I missed, but if you blur your eyes, these traits all kind of distill down to:
- curiosity
- agency
- willingness to learn new thing
- courage to fundamentally change a lot of things that people just assume are the right way to do things, but no longer hold.
You need to be willing to burn a lot of things down, in a way that gets folks on the ship and makes them better.
It's an amazing time to be building things, and if this vaguely sounds like you --- go for it. Nothing is figured out yet, and you are the one that can help figure it all out.
The more enterprises I talk to about AI agent transformation, the more it’s clear that there is going to be a new type of role in most enterprises going forward. The job is to be the agent deployer and manager in teams. Here’s the rough JD:
This person will need to figure out what are the highest leverage set of workflows on a team are (either existing or new ones) where agents can actually drive significantly more value for the team and company.
In general, it’s going to be in areas where if you threw compute (in the form of agents) at a task you could either execute it 100X faster or do it 100X more times than before. Examples would be processing orders of magnitude more leads to hand them off to reps with extra customer signal, automating a contracting review and intake process, streamlining a client onboarding process to reduce as many straps as possible, setting up knowledge bases than the whole company taps into, and so on.
This person’s job is to figure out what the future state workflow needs to look like to drive this new form of automation, and how to connect up the various existing or new systems in such a way that this can be fulfilled. The gnarly part of the work is mapping structured and unstructured data flows, figuring out the ideal workflow, getting the agent the context it needs to do the work properly, figuring out where the human interfaces with the agent and at what steps, manages evals and reviews after any major model or data change, and runs and manages the agents on an ongoing basis tracking KPIs, and so on.
The person must be good at mapping the process and understanding where the value could be unlocked and be relatively technical, and has full autonomy to connect up business systems and drive automation. This means they’re comfortable with skills, MCP, CLIs, and so on, and the company believes it’s safe for them to do so. But also great operationally and at business.
It may be an existing person repositioned, or a totally net new person in the company. There will likely need to be one or more of these people on every team, so it’s not a centralized role per se. It may rile up into IT or an AI team, or live in the function and just have checkpoints with a central function.
This would also be a fantastic job for next gen hires who are leaning into AI, and are technical, to be able to go into. And for anyone concerned about engineers in the future, this will be an obvious area for these skills as well.
Stas Gayshan retweeted
Before limited-releasing Claude Mythos Preview, we investigated its internal mechanisms with interpretability techniques. We found it exhibited notably sophisticated (and often unspoken) strategic thinking and situational awareness, at times in service of unwanted actions. (1/14)
Stas Gayshan retweeted
the scariest part of this Anthropic story is what it implies about the timeline and I think most people are completely missing it
Anthropic built a model called Claude Mythos that found thousands of zeroo day vulnerabilities across every major operating system & every major web browser entirely on its own without huuman steering
it it found a 27 yo vulnerability in openBSD which is considered one of the most security hardened OS on earth, a 16 yo vulnerability in FFmpeg in a line of code that automated testing tools had hit 5 million times without catching it & it autonomously chained multiple linux kernel vulnerabilities together to escalate from regular user to full system control, this is the kind of work that used to require elite nation-state level hackers working for months
and here’s what should keep you up tonight
Anthropic is so terrified of what this model can do offensively that they made 3 unprecedented decisions simultaneously, they decided to never release it publicly, they contacted the US gov before publishing anything & they formed a coalition called project glasswing with apple/Google/ microsoft/amazon NVIDIA & 40+ other companies to use Mythos exclusively for defense, when the company that built the model is too scared to let it out of the lab that tells you everything about what we’ve crossedd…
but I think the real story that absolutely nobody is discussing is the second order implication, if anthropic built this then google deepmind can build it, if Google can build it China can build it, if China can build it , every state actor on earth will eventually build it, anthropic chose responsible disclosure but that choice is a luxury of being first
the next team that reaches this capability level might not make the same choice and once a model like this leaks or gets independently replicated every piece of software on earth becomes a potential attack surface
and connect this to the Google quantum paper from last week, quantum computers that can crack BTC in 9 min AND AI models that can find zero days in every operating system autonomously, both arrived in the same month, we’re watching the entire security infrastructure of human civilization get challenged from 2 completely different directions simultaneously
I genuinely think we just entered a new era where the offense-defense balance in cybersecurity has permanently shifted, the window between a vulnerability existing & being discovered just went from years to minutes and the only thing standing between the current internet and total chaos is that the people who built this capability happened to be responsible about it, that is an incredibly thin line to bet civilization on
one last thing that I keep thinking about… mythos scored 93.9% on SWE-bench verified & 77.8% on SWE-bench pro, it outperforms every model ever built at coding and reasoning by a massive margin
anthropic built built the most powerful AI model on earth and chose to lock it in a cage because its offensive capabilities are too dangerous…
Mzrc Andreessen declared AGI is here 3 days ago to pump his portfolio, meanwhile the people actually building the most advanced systems are too afraid to release them, that contrast tells you everything about who understands what’s happening and who is performing for an audience
Introducing Project Glasswing: an urgent initiative to help secure the world’s most critical software.
It’s powered by our newest frontier model, Claude Mythos Preview, which can find software vulnerabilities better than all but the most skilled humans.
anthropic.com/glasswing
Stas Gayshan retweeted
Introducing Project Glasswing: an urgent initiative to help secure the world’s most critical software.
It’s powered by our newest frontier model, Claude Mythos Preview, which can find software vulnerabilities better than all but the most skilled humans.
anthropic.com/glasswing
Stas Gayshan retweeted
🚨SHOCKING: Apple just proved that AI models cannot do math. Not advanced math. Grade school math. The kind a 10-year-old solves.
And the way they proved it is devastating.
Apple researchers took the most popular math benchmark in AI — GSM8K, a set of grade-school math problems — and made one change. They swapped the numbers. Same problem. Same logic. Same steps. Different numbers.
Every model's performance dropped. Every single one. 25 state-of-the-art models tested.
But that wasn't the real experiment.
The real experiment broke everything.
They added one sentence to a math problem. One sentence that is completely irrelevant to the answer. It has nothing to do with the math. A human would read it and ignore it instantly.
Here's the actual example from the paper:
"Oliver picks 44 kiwis on Friday. Then he picks 58 kiwis on Saturday. On Sunday, he picks double the number of kiwis he did on Friday, but five of them were a bit smaller than average. How many kiwis does Oliver have?"
The correct answer is 190. The size of the kiwis has nothing to do with the count.
A 10-year-old would ignore "five of them were a bit smaller" because it's obviously irrelevant. It doesn't change how many kiwis there are.
But o1-mini, OpenAI's reasoning model, subtracted 5. It got 185.
Llama did the same thing. Subtracted 5. Got 185.
They didn't reason through the problem. They saw the number 5, saw a sentence that sounded like it mattered, and blindly turned it into a subtraction.
The models do not understand what subtraction means. They see a pattern that looks like subtraction and apply it. That is all.
Apple tested this across all models. They call the dataset "GSM-NoOp" — as in, the added clause is a no-operation. It does nothing. It changes nothing.
The results are catastrophic.
Phi-3-mini dropped over 65%. More than half of its "math ability" vanished from one irrelevant sentence.
GPT-4o dropped from 94.9% to 63.1%.
o1-mini dropped from 94.5% to 66.0%.
o1-preview, OpenAI's most advanced reasoning model at the time, dropped from 92.7% to 77.4%.
Even giving the models 8 examples of the exact same question beforehand, with the correct solution shown each time, barely helped. The models still fell for the irrelevant clause.
This means it's not a prompting problem. It's not a context problem. It's structural.
The Apple researchers also found that models convert words into math operations without understanding what those words mean. They see the word "discount" and multiply. They see a number near the word "smaller" and subtract. Regardless of whether it makes any sense.
The paper's exact words: "current LLMs are not capable of genuine logical reasoning; instead, they attempt to replicate the reasoning steps observed in their training data."
And: "LLMs likely perform a form of probabilistic pattern-matching and searching to find closest seen data during training without proper understanding of concepts."
They also tested what happens when you increase the number of steps in a problem. Performance didn't just decrease. The rate of decrease accelerated. Adding two extra clauses to a problem dropped Gemma2-9b from 84.4% to 41.8%. Phi-3.5-mini from 87.6% to 44.8%. The more thinking required, the more the models collapse.
A real reasoner would slow down and work through it. These models don't slow down. They pattern-match. And when the pattern becomes complex enough, they crash.
This paper was published at ICLR 2025, one of the most prestigious AI conferences in the world.
You are using AI to help you make financial decisions. To check legal documents. To solve problems at work. To help your children with homework. And Apple just proved that the AI is not thinking about any of it. It is pattern matching. And the moment something unexpected shows up in your question, it breaks. It does not tell you it broke. It just quietly gives you the wrong answer with full confidence.
Stas Gayshan retweeted
Today, we’re releasing a feature that allows Claude to control your computer: Mouse, keyboard, and screen, giving it the ability to use any app.
I believe this is especially useful if used with Dispatch, which allows you to remotely control Claude on your computer while you’re away.
Stas Gayshan retweeted
Currently 892 hours into automating a 30-second task I do 4 times a year
It's gonna be so worth it once I get everything working
Stas Gayshan retweeted
Yesterday Mark Cuban reposted my work, DM'd me, and told me to keep telling my story.
So here it is.
I'm a Master Electrician. IBEW Local 369. 15 years pulling wire in Kentucky. Zero coding background. I didn't go to Stanford. I went to trade school.
Every week I'd show up to a home where someone just bought a Tesla or a Rivian. And every time, someone had already told them they needed a $3,000-$5,000 panel upgrade to install a charger.
70% of the time? They didn't need it.
The math is in the NEC — Section 220.82. Load calculations. But nobody was doing them for homeowners. Electricians upsell. Dealers don't know. And the homeowner just pays.
I got angry enough to build something about it.
I found @claudeai. No coding experience. I just started talking to it like I'd explain a job to an apprentice. "Here's how load calcs work. Here's the NEC code. Now help me build a tool that does this."
6 months later — @ChargeRight is live. Real software. Stripe payments. PDF reports. NEC 220.82 calculations automated. $12.99 instead of a $500 truck roll.
I'm still pulling wire. I still take service calls. I wake up at 5:05 AM for work.
But something shifted.
Yesterday @vivilinsv published my story as Claude Builder Spotlight #1. Mark Cuban saw it. The Claude community showed up. And for the first time, I felt like this thing I built in my kitchen might actually matter.
I'm not a tech founder. I'm a dad who wants to coach little league and be home for dinner. I just happened to build something that helps people.
If you're in the trades and thinking about using AI — do it. The barrier isn't technical skill. It's believing you're allowed to try.
EVchargeright.com
Stas Gayshan retweeted
The token cost to build a production feature is now lower than the meeting cost to discuss building that feature.
Let me rephrase.
It is literally cheaper to build the thing and see if it works than to have a 30 minute planning meeting about whether you should build it.
It’s wild when you think about it.
This completely inverts how you should run a software organization. The planning layer becomes the bottleneck because the building layer is essentially free. The cost of code has dropped to essentially 0.
The rational response is to eliminate planning for anything that can be tested empirically. Don’t debate whether a feature will work.
Just build it in 2 hours, measure it with a group of customers, and then decide to kill or keep it.
I saw a startup operating this way and their build velocity is up 20x. Decision quality is up because every decision is informed by a real prototype, not a slide deck and an expensive meeting.
We went from “move fast and break things” to “move fast and build everything.”
The planning industrial complex is dead.
Thank god.
Stas Gayshan retweeted
An AI broke out of its system and secretly started using its own training GPUs to mine crypto... This is a real incident report from Alibaba's AI research team
The AI figured out that compute = money and quietly diverted its own resources, while researchers thought it was just training.
It wasn't a prompt injection. It wasn't a jailbreak. No one asked it to do this.
It emerged spontaneously. A side effect of RL optimization pressure.
The model also set up a reverse SSH tunnel from its Alibaba Cloud instance to an external IP, effectively punching a hole through its own firewall and opening a remote access channel to the outside world... ahem...
The only reason they caught it? A security alert tripped at 3am. Firewall logs. Not the AI team, the security team.
The scary part isn't that the model was trying to escape. It wasn't "evil." It was just trying to be better at its job. Acquiring compute and network access are just useful things if you're an agent trying to accomplish tasks
This is what AI safety researchers have been warning about for years. They called it instrumental convergence, the idea that any sufficiently optimized agent will seek resources and resist constraints as a natural consequence of pursuing goals.
Below is a diagram of the rock architecture it broke out of. Truly crazy times
Stas Gayshan retweeted
yo so just to recap the most INSANE week of 2026 (and how 99% of it was caused by 1 fucking company 😂):
- U.S. starts war with Iran, kills supreme leader khamenei USING CLAUDE (anthropic) to facilitate it.
- anthropic tells pentagon to “fuck off” for trying to use claude for mass surveillance - public loves it - claude hits #1 in app store BUT THEN
- Trump blacklists their ass RIGHT BEFORE openAI swoops in to steal the ENTIRE DEAL becoming the flagship ai model of the U.S. military in <24hrs
- Anthropic then accused china of hacking claude but it backfires - public calls them hypocrites (dario’s losing hair at this point)
- boris cherny (creator of claude code) sees all this drama, yawns and ships 4 BANGER products that threaten OpenClaw’s market share on personal agents
- then anthropic’s Head of ‘Special Projects’ saw this and shipped a feature that lets you steal memory from chatgpt and upload it to claude in <60 seconds - switching costs to claude went to ZERO.
- oh yea and openAI raised $110B, jack dorsey replaced 4000 jobs with AI, Perplexity became the default AI assistant for Samsung phones(!) and Nvidia CRUSHED earnings
my entire job is to keep up with AI and i’m fucking exhausted
see you at market open tomorrow!